The genome of each individual harbors millions of nucleotide variants, and a major challenge is to understand how these variants contribute to phenotypic variations in the population. We propose a combined computational and experimental framework for identifying non-coding variants that affect cellular and physiological traits, with the goal to establish computational models that can predict the probability of exhibiting a physiological trait from the sequences of non-coding genomic regions. This framework involves iterative refinement of model assumptions and parameters with experimentation. To develop the framework and validate the predictive models, we will focus on the disease Age-related Macular Degeneration (AMD), the leading cause of blindness among the elderly in the country. Previous studies have identified a number of sequence variants strongly associated with AMD. We will develop computational models to predict (or narrow down) the set of non-coding sequence variants that contribute to the disease phenotype. As experimental assessment, we will perform genome editing in patient-derived induced pluripotent stem cells (iPSC) to test the consequence of removing or introducing such sequence variants on molecular and cellular phenotypes in cell culture and in rodent models. While the proposed method is developed for AMD, the general approach is expected to apply to other genetic diseases.